1. Technical Field
The present invention relates to image segmentation, and more particularly, to a method for automatic separation of segmented tubular and circular objects.
2. Discussion of the Related Art
In general, segmentations of branching or multiple tubular objects in close proximity to each other relative to the resolution of an imaging device result in the fusing together of originally distinct structures, Image noise, partial volume artifacts, and other factors can also contribute to the fusing together of such structures. An example of the fusing together of originally distinct structures involves the segmentation of the vessels within the lungs. Here, the arteries and veins are frequently segmented together as a single component. Similar situations are also found within arteries and veins throughout the body. As a result, the goal of many researchers is to get a segmentation that illustrates the arteries and/or veins separately.
The ability to create separate segmentations for the arteries and veins can allow for improvements in visualization and Computer Aided Detection (CAD) by eliminating unneeded data. For example, pulmonary embolism (PE) is a condition where a clot occurs within the arteries of the lungs. An examination to determine if a PE exists does not require a physician to search within the veins. Hence, if only the arteries were identified and the veins eliminated, the search would be simplified.
Approaches to artery-vein segmentation can involve physical methods for discrimination. These methods depend on differences in the direction of flow, differences in the uptake of contrast agents, or measurements of blood oxygen levels. However, these approaches require specific contrast agents, complex acquisitions, or limit the imaging modality that can be used. Recently, image processing approaches have been offered that allow for more generalized application with less of these physical limitations. However, manual user interaction requirements and limited applicability can be issues for these methods.
For example, the method presented in T. Lei, J. K. Udupa, P. K. Saha, and D. Odhner, “Artery-Vein Separation via MRA—An Image Processing Approach,” IEEE TMI, Vol. 20., No. 8, August 2001, makes use of fuzzy connectivity to define arteries and veins within Magnetic Resonance Angiography (MRA) image data. Here, the user must specify several points within the image to delineate the arteries and veins. These points are then used to compete against each other within the segmentation. No notion of a tubular bifurcating structure is used in this method. Another approach involving MRA image data that makes use of level set techniques is described in C. M. van Bemmel, L. J. Spreeuwers, M. A. Viergever, and W. J. Niessen, “Level-Set-Based Artery-Vein Separation in Blood Pool Agent CE-M JR Angiograms,” IEEE TMI, Vol. 22, No. 10, October 2003. This method makes use of a vessel-like model though level sets and uses a line-filter for segmentation. However, the user must specify paths within both the arterial and venous trees. These requirements of manual interaction limit the possibilities for applications such as CAD or even in cases where further processing is required once manual input is obtained.
An automatic approach for artery-vein segmentation within the lungs of computed tomography (CT) images was presented in T. Bulow, R. Wiemaker, T. Blaffert, C. Lorenz, and S. Renisch, “Automatic Extraction of the Pulmonary Artery Tree from Multi-Slice CT Data,” SPIE Medical Imaging 2005: Physiology, Function, and Structure from Medical Images, pp. 730-740, 2005. This method makes use of the airways to help isolate arteries from the veins. Here, a tree model of the segmented vessels is evaluated at each point to determine if nearby airways follow these vessels. Vessels with a higher measure of likely airways are determined to be arteries and all descendants are classified as such. This method, however, is limited to the lungs.
A generalized approach for tree separation in segmented structures was presented in S. R. Aylward and E. Bullitt, “Initialization, Noise, Singularities, and Scale in Height Ridge Transversal for Tubular Object Centerline Extraction,” IEEE TMI, Vol. 21, No. 2, February 2002 and U.S. Patent Application Publication No. 2006/0056685, “Method and Apparatus for Embolism Analysis”, filed Aug. 12, 2005. In the method disclosed in U.S. Patent Application Publication No. 2006/0056685, a tree model was fitted to a segmentation and intersecting branches from other tree structures were detected and eliminated based upon expected normal branch angles. This method was applied to a sub-tree of an entire tree structure that was manually chosen by a user. Although this method is promising, a fully automatic approach is desired for some applications, since applying this method to an entire tree structure may be time consuming given the complexity of the tree structure.